Feed efficiency (FE) is critical in pig production for both economic and environmental reasons. As the intestinal microbiota plays an important role in energy harvest, it is likely to influence FE. Therefore, our aim was to characterize the intestinal microbiota of pigs ranked as low, medium, and high residual feed intake ([RFI] a metric for FE), where genetic, nutritional, and management effects were minimized, to explore a possible link between the intestinal microbiota and FE. Eightyone pigs were ranked according to RFI between weaning and day 126 postweaning, and 32 were selected as the extremes in RFI (12 low, 10 medium, and 10 high). Intestinal microbiota diversity, composition, and predicted functionality were assessed by 16S rRNA gene sequencing. Although no differences in microbial diversity were found, some RFI-associated compositional differences were revealed, principally among members of Firmicutes, predominantly in feces at slaughter (albeit mainly for low-abundance taxa). In particular, microbes associated with a leaner and healthier host (e.g., Christensenellaceae, Oscillibacter, and Cellulosilyticum) were enriched in low RFI (more feed-efficient) pigs. Differences were also observed in the ileum of low RFI pigs; most notably, Nocardiaceae (Rhodococcus) were less abundant. Predictive functional analysis suggested improved metabolic capabilities in these animals, especially within the ileal microbiota. Higher ileal isobutyric acid concentrations were also found in low RFI pigs. Overall, the differences observed within the intestinal microbiota of low RFI pigs compared with that of their high RFI counterparts, albeit relatively subtle, suggest a possible link between the intestinal microbiota and FE in pigs.IMPORTANCE This study is one of the first to show that differences in intestinal microbiota composition, albeit subtle, may partly explain improved feed efficiency (FE) in low residual feed intake (RFI) pigs. One of the main findings is that, although microbial diversity did not differ among animals of varying FE, specific intestinal microbes could potentially be linked with porcine FE. However, as the factors impacting FE are still not fully understood, intestinal microbiota composition may not be a major factor determining differences in FE. Nonetheless, this work has provided a potential set of microbial biomarkers for FE in pigs. Although culturability could be a limiting factor and intervention studies are required, these taxa could potentially be targeted in the future to manipulate the intestinal microbiome so as to improve

BackgroundResearch on wild animal ecology is increasingly employing GPS telemetry in order to determine animal movement. However, GPS systems record position intermittently, providing no information on latent position or track tortuosity. High frequency GPS have high power requirements, which necessitates large batteries (often effectively precluding their use on small animals) or reduced deployment duration. Dead-reckoning is an alternative approach which has the potential to ‘fill in the gaps’ between less resolute forms of telemetry without incurring the power costs. However, although this method has been used in aquatic environments, no explicit demonstration of terrestrial dead-reckoning has been presented.ResultsWe perform a simple validation experiment to assess the rate of error accumulation in terrestrial dead-reckoning. In addition, examples of successful implementation of dead-reckoning are given using data from the domestic dog Canus lupus, horse Equus ferus, cow Bos taurus and wild badger Meles meles.ConclusionsThis study documents how terrestrial dead-reckoning can be undertaken, describing derivation of heading from tri-axial accelerometer and tri-axial magnetometer data, correction for hard and soft iron distortions on the magnetometer output, and presenting a novel correction procedure to marry dead-reckoned paths to ground-truthed positions. This study is the first explicit demonstration of terrestrial dead-reckoning, which provides a workable method of deriving the paths of animals on a step-by-step scale. The wider implications of this method for the understanding of animal movement ecology are discussed.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0055-4) contains supplementary material, which is available to authorized users.

BackgroundAccelerometers are powerful sensors in many bio-logging devices, and are increasingly allowing researchers to investigate the performance, behaviour, energy expenditure and even state, of free-living animals. Another sensor commonly used in animal-attached loggers is the magnetometer, which has been primarily used in dead-reckoning or inertial measurement tags, but little outside that. We examine the potential of magnetometers for helping elucidate the behaviour of animals in a manner analogous to, but very different from, accelerometers. The particular responses of magnetometers to movement means that there are instances when they can resolve behaviours that are not easily perceived using accelerometers.MethodsWe calibrated the tri-axial magnetometer to rotations in each axis of movement and constructed 3-dimensional plots to inspect these stylised movements. Using the tri-axial data of Daily Diary tags, attached to individuals of number of animal species as they perform different behaviours, we used these 3-d plots to develop a framework with which tri-axial magnetometry data can be examined and introduce metrics that should help quantify movement and behaviour.ResultsTri-axial magnetometry data reveal patterns in movement at various scales of rotation that are not always evident in acceleration data. Some of these patterns may be obscure until visualised in 3D space as tri-axial spherical plots (m-spheres). A tag-fitted animal that rotates in heading while adopting a constant body attitude produces a ring of data around the pole of the m-sphere that we define as its Normal Operational Plane (NOP). Data that do not lie on this ring are created by postural rotations of the animal as it pitches and/or rolls. Consequently, stereotyped behaviours appear as specific trajectories on the sphere (m-prints), reflecting conserved sequences of postural changes (and/or angular velocities), which result from the precise relationship between body attitude and heading. This novel approach shows promise for helping researchers to identify and quantify behaviours in terms of animal body posture, including heading.ConclusionMagnetometer-based techniques and metrics can enhance our capacity to identify and examine animal behaviour, either as a technique used alone, or one that is complementary to tri-axial accelerometry.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-017-0097-x) contains supplementary material, which is available to authorized users.

BackgroundSmart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals behave. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information.DescriptionThis work presents Framework4, an all-encompassing software suite which operates on smart sensor data to determine the 4 key elements considered pivotal for movement analysis from such tags (Endangered Species Res 4: 123-37, 2008). These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal moves. The program transforms smart sensor data into dead-reckoned movements, template-matched behaviours, dynamic body acceleration-derived energetics and position-linked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space.ConclusionsFramework4 is a user-friendly software that assists biologists in elucidating 4 key aspects of wild animal ecology using data derived from tags with multiple sensors recording at high rates. Its use should enhance the ability of biologists to derive meaningful data rapidly from complex data.

Intestinal microbe-host interactions can affect the feed efficiency (FE) of chickens. As inconsistent findings for FE-associated bacterial taxa were reported across studies, the present objective was to identify whether bacterial profiles and predicted metabolic functions that were associated with residual feed intake (RFI) and performance traits in female and male chickens were consistent across two different geographical locations. At six weeks of life, the microbiota in ileal, cecal and fecal samples of low (n = 34) and high (n = 35) RFI chickens were investigated by sequencing the V3-5 region of the 16S rRNA gene. Location-associated differences in α-diversity and relative abundances of several phyla and genera were detected. RFI-associated bacterial abundances were found at the phylum and genus level, but differed among the three intestinal sites and between males and females. Correlation analysis confirmed that, of the taxonomically classifiable bacteria, Lactobacillus (5% relative abundance) and two Lactobacillus crispatus-OTUs in feces were indicative for high RFI in females (P < 0.05). In males, Ruminococcus in cecal digesta (3.1% relative abundance) and Dorea in feces (<0.1% relative abundance) were best indicative for low RFI, whereas Acinetobacter in feces (<1.5% relative abundance) related to high RFI (P < 0.05). Predicted metabolic functions in feces of males confirmed compositional relationships as functions related to amino acid, fatty acid and vitamin metabolism correlated with low RFI, whereas an increasing abundance of bacterial signaling and interaction (i.e. cellular antigens) genes correlated with high RFI (P < 0.05). In conclusion, RFI-associated bacterial profiles could be identified across different geographical locations. Results indicated that consortia of low-abundance taxa in the ileum, ceca and feces may play a role for FE in chickens, whereby only bacterial FE-associations found in ileal and cecal digesta may serve as useful targets for dietary strategies.

This study is based on extensive studies in the mouse of the role of CSF1 in monocyte-macrophage production and differentiation and the function of macrophages in the control of hepatocyte proliferation. We use a novel form of CSF1, an Fc fusion protein, to demonstrate that the findings in mice can be extended to large animals. We discuss the possible role for CSF1 in homeostatic control of the size of the liver.

Optimal feed efficiency (FE) in pigs is important for economic and environmental reasons. Previous research identified FE-associated bacterial taxa within the intestinal microbiota of growing pigs. This study investigated whether FE-associated bacteria and selected FE-associated physiological traits were consistent across geographic locations (Republic of Ireland [ROI] [two batches of pigs, ROI1 and ROI2], Northern Ireland [NI], and Austria [AT]), where differences in genetic, dietary, and management factors were minimized. Pigs (n = 369) were ranked, within litter, on divergence in residual feed intake (RFI), and 100 extremes were selected (50 with high RFI and 50 with low RFI) across geographic locations for intestinal microbiota analysis using 16S rRNA amplicon sequencing and examination of FE-associated physiological parameters. Microbial diversity varied by geographic location and intestinal sampling site but not by RFI rank, except in ROI2, where more-feed-efficient pigs had greater ileal and cecal diversity. Although none of the 188 RFI-associated taxonomic differences found were common to all locations/batches, Lentisphaerae, Ruminococcaceae, RF16, Mucispirillum, Methanobrevibacter, and two uncultured genera were more abundant within the fecal or cecal microbiota of low-RFI pigs in two geographic locations and/or in both ROI batches. These are major contributors to carbohydrate metabolism, which was reflected in functional predictions. Fecal volatile fatty acids and salivary cortisol were the only physiological parameters that differed between RFI ranks. Despite controlling genetics, diet specification, dietary phases, and management practices in each rearing environment, the rearing environment, encompassing maternal influence, herd health status, as well as other factors, appears to impact intestinal microbiota more than FE. IMPORTANCE Interest in the role of intestinal microbiota in determining FE in pigs has increased in recent years. However, it is not known if the same FE-associated bacteria are found across different rearing environments. In this study, geographic location and intestinal sampling site had a greater influence on the pig gut microbiome than FE. This presents challenges when aiming to identify consistent reliable microbial biomarkers for FE. Nonetheless, seven FE-associated microbial taxa were common across two geographic locations and/or two batches within one location, and these indicated a potentially “healthier” and metabolically more capable microbiota in more-feed-efficient pigs. These taxa could potentially be employed as biomarkers for FE, although bacterial consortia, rather than individual taxa, may be more likely to predict FE. They may also merit consideration for use as probiotics or could be targeted by dietary means as a strategy for improving FE in pigs in the future.

Chickens with good or poor feed efficiency (FE) have been shown to differ in their intestinal microbiota composition. This study investigated differences in the fecal bacterial community of highly and poorly feed-efficient chickens at 16 and 29 days posthatch (dph) and evaluated whether a fecal microbiota transplant (FMT) from feed-efficient donors early in life can affect the fecal microbiota in chickens at 16 and 29 dph and chicken FE and nutrient retention at 4 weeks of age. A total of 110 chickens were inoculated with a FMT or a control transplant (CT) on dph 1, 6, and 9 and ranked according to residual feed intake (RFI; the metric for FE) on 30 dph. Fifty-six chickens across both inoculation groups were selected as the extremes in RFI (29 low, 27 high). RFI-related fecal bacterial profiles were discernible at 16 and 29 dph. In particular, ,, and operational taxonomic units were associated with low RFI (good FE). Multiple administrations of the FMT only slightly changed the fecal bacterial composition, which was supported by weighted UniFrac analysis, showing similar bacterial communities in the feces of both inoculation groups at 16 and 29 dph. Moreover, the FMT did not change the RFI and nutrient retention of highly and poorly feed-efficient recipients, whereas it tended to increase feed intake and body weight gain in female chickens. This finding suggests that host- and environment-related factors may more strongly affect chicken fecal microbiota and FE than the FMT. Modulating the chicken's early microbial colonization using a FMT from highly feed-efficient donor chickens may be a promising tool to establish a more desirable bacterial profile in recipient chickens, thereby improving host FE. Although FE-associated fecal bacterial profiles at 16 and 29 dph could be established, the microbiota composition of a FMT, when administered early in life, may not be a strong factor modulating the fecal microbiota at 2 to 4 weeks of life and reducing the variation in chicken's FE. Nevertheless, the present FMT may have potential benefits for growth performance in female chickens.